Performance of the multi-stage variable group hybrid interference cancellation scheme with timing and phase errors
Bibliographic record
Abstract
The variable group hybrid interference cancellation (VGHIC) scheme proposed in [KW Ang et al., 1999] outperformed both the successive (SIC) and parallel (PIC) interference cancellation in a system with perfect fast power control, and also in a system without fast power control. However, the comparison was done under the assumption that the timing and phase estimations were perfect. In practical systems, these parameters are not perfectly estimated. In this paper, we study the effect of phase and timing errors on the bit error rate (BER) performance of the VGHIC and its multi-stage structure. A performance comparison with the SIC, PIC and the conventional matched filter receiver is also presented. Simulations show that the VGHIC maintains an overall performance advantage over the SIC and PIC even with high estimation errors. However, the performance gain from using the multi-stage structure diminishes rapidly as the estimation errors are increased. It is observed that the rapidly as the estimation errors are increased. It is observed that the VGHIC, as well as the PIC and SIC are more tolerant of phase errors than timing errors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".